Real Examples of Why We Need Context for Responsible AI

Read part one of this two-part series on responsible AI. Discover real-world examples of the myths,., biases and more that currently surround AI.

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How Boston Scientific Improves Manufacturing Quality Using Graph Analytics

Editor’s Note: This presentation was given by Eric Wespi and Eric Spiegelburg at GraphConnect New York in September 2018. Presentation Summary We’re going to talk about some project themes that make sense when you’re going about developing a graph project.… Read more →

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Live from Lyft: Q&A with Mark Grover, Product Manager at Lyft

Discover how Lyft has integrated Neo4j into their data sharing process and how they test information integrity with Amundsen.

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How Graphs Enhance Artificial Intelligence

Explore the increasing impact of graph technology on artificial intelligence and the steps toward enhancing AI and ML with a graph database.

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DeepWalk: Implementing Graph Embeddings in Neo4j

Discover tips and strategies for implementing graph embedding into a Neo4j graph database, with plenty of Cypher examples.

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AI & Graph Technology: AI Explainability

Read the fifth and final installment of our blog series on artificial intelligence and the ways AI explainability adds context for credibility.

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AI & Graph Technology: Connections Improve Accuracy

Read the fourth installment of this blog series on artificial intelligence on the ways connected feature extraction analyzes data.

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Visualizing This Week in Tech

Learn how to visualize common elements amongst the barrage of weekly tech stories with knowledge graphs and machine learning techniques.

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AI & Graph Technology: What Are Knowledge Graphs?

Read the second installment of this blog series on artificial intelligence on the ways knowledge graphs add context for decision support.

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AI and Graph Technology: 4 Ways Graphs Add Context

Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.

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Toward AI Standards: Context Makes AI More Reliable and Trustworthy

Discover why native graph technology is the key to making artificial intelligence and machine learning more reliable and trustworthy.

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Graphs in Banking: Integration with AI & Machine Learning [Video]

Check out this webinar on graph technology within the financial services industry, featuring use cases on fraud detection, MDM and IAM.

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Toward AI Standards: Context Makes AI More Robust

Read this third blog in our series “Toward AI Standards” and discover how graph technology adds context to data relationships for more robust AI solutions.

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Deciphering Product DNA: Next-Level PDM with AI & Knowledge Graphs

Increasingly complex products undoubtedly require greater management of components, function and data. Classic product data management (PDM) has long reach its limits in this respect. Breaking down product DNA is now driven by artificial intelligence (AI) and knowledge graphs. In… Read more →

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Liberating Knowledge: Machine Learning Techniques with Neo4j

Explore how Hume leverages natural language understanding, graph analytics and AI capabilities to connect data and share knowledge.

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Toward AI Standards: Graph Technology for Responsible AI

Read the first installment of this four-part series on how graphs provide the context artificial intellingence needs to remain reliable and trustworthy.

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Graphs in Automotive & Manufacturing: Unlock New Value from Your Data [Video]

Check out this webinar on using graph database technology in the manufacturing and automotive industries, including the use of graph algorithms in AI.

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Toward AI Standards: Why Context Is Critical for Artificial Intelligence

Learn what Neo4j CEO Emil Eifrem believes is the most critical component of both technical and ethical standards for AI – and how we might make it happen.

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Improving Machine Learning Predictions Using Graph Algorithms [Video]

Watch this webinar on how graph algorithms are reinventing the field of intelligent applications by improving the predictability of AI and ML models.

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From Collections to Connections: Where Hadoop Adoption Goes from Here

Learn about the evolving world of big data and how the industry is increasingly interested in connecting data rather than just collecting it.

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The Present and Future of Artificial Intelligence and Machine Learning

Discover how we can methodically explore capabilities related to artificial intelligence and machine learning to successfully predict the future.

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NODES 2019: Launching the Neo4j Online Developer Expo & Summit!

Discover what’s in store for Neo4j’s first-ever online summit built just for developers, engineers, data scientists and graphistas from around the globe.

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15 Years Loving Graphs: Big Congrats & Thanks to Our Italian Partner LARUS

I just returned from Venice, where enjoyed not only the weather and food but …​ Had an amazing time in Venice, Italy to celebrate the 15th anniversary of our friends and partners from @AgileLARUS. Thanks a lot @inserpio for the… Read more →

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Think About What You’re Building: 5-Minute Interview with Stephen O’Grady

Check out this 5-minute interview with Stephen O’Grady, Co-Found of Redmonk, on how developers should consider the broader implications of what they build.

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#GraphCast: How Graphs Are Used around the Globe

Check out this week’s #GraphCast, featuring graph technology users from around the world talking about how they use Neo4j to solve connected data problems.

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